Update app.py

app.py

@@ -1,55 +1,21 @@
+import gradio as gr
+from fastapi import FastAPI
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
-from langchain.schema.messages import HumanMessage, SystemMessage
-from langchain.schema.document import Document
 from langchain.vectorstores import FAISS
-from langchain.retrievers.multi_vector import MultiVectorRetriever
-import os
-import uuid
-import base64
-from fastapi import FastAPI, Request, Form, Response, File, UploadFile
-from fastapi.responses import HTMLResponse, JSONResponse
-from fastapi.templating import Jinja2Templates
-from fastapi.encoders import jsonable_encoder
-from fastapi.middleware.cors import CORSMiddleware
-import json
 from dotenv import load_dotenv
+import os
+
 load_dotenv()
 
 app = FastAPI()
-templates = Jinja2Templates(directory="templates")
-
-# Configure CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  
-    allow_credentials=True,
-    allow_methods=["*"], 
-    allow_headers=["*"],
-)
-
-# Securely retrieve the OpenAI API key
-openai_api_key = os.getenv("OPENAI_API_KEY")
-import os
-
-# Securely retrieve the OpenAI API key from the environment variable
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
-if not openai_api_key:
-    raise ValueError("Missing OpenAI API key. Set OPENAI_API_KEY in your environment variables.")
-openai_api_key = os.getenv("OPENAI_API_KEY")
-if openai_api_key:
-    print("API Key loaded successfully!")
-else:
-    print("API Key not found.")
 embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
-
-
 db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
 
-# Define the prompt template
 prompt_template = """
 You are an expert in skin cancer, etc.
 Answer the question based only on the following context, which can include text, images, and tables:
@@ -63,15 +29,10 @@ Just return the helpful answer in as much detail as possible.
 Answer:
 """
 
-qa_chain = LLMChain(llm=ChatOpenAI(model="gpt-4", openai_api_key = openai_api_key, max_tokens=1024),
+qa_chain = LLMChain(llm=ChatOpenAI(model="gpt-4", openai_api_key=openai_api_key, max_tokens=1024),
                     prompt=PromptTemplate.from_template(prompt_template))
 
-@app.get("/", response_class=HTMLResponse)
-async def index(request: Request):
-    return templates.TemplateResponse("index.html", {"request": request})
-
-@app.post("/get_answer")
-async def get_answer(question: str = Form(...)):
+def get_answer(question: str):
     relevant_docs = db.similarity_search(question)
     context = ""
     relevant_images = []
@@ -84,4 +45,10 @@ async def get_answer(question: str = Form(...)):
             context += '[image]' + d.page_content
             relevant_images.append(d.metadata['original_content'])
     result = qa_chain.run({'context': context, 'question': question})
-    return JSONResponse({"relevant_images": relevant_images[0], "result": result})
+    return {"relevant_images": relevant_images[0], "result": result}
+
+iface = gr.Interface(fn=get_answer, inputs="text", outputs="json")
+
+# Run the Gradio interface inside FastAPI
+if __name__ == "__main__":
+    iface.launch()